Py
Pythonic Interfaces Using Decorators
Pythonic Interfaces Using Decorators
Share cardActual performance
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Launch Intel predictions
Analyze your own launch →86%86% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
16%16% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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